Mythos-Class Models

Mis à jour le 2025-01-04Confiance : high

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anthropic's designation for their largest and most capable language models, representing approximately 2x the scale of previous Opus-class models. The first Mythos-class models include claude-fable 5 (general availability) and claude-mythos 5 (restricted access).

Model Specifications

Scale: At least 2x the size of Opus-class models Architecture: Transformer-based with enhanced capabilities for long-horizon tasks Context Window: 1M tokens maintained from Opus generation Dual Deployment: Both general availability (Fable) and restricted access (Mythos) variants

Key Capabilities

Benchmark Performance

Mythos-class models achieved state-of-the-art performance across multiple domains:

SWE-Bench Pro: 80.3% (21.7 point lead over GPT-5.5)
FrontierCode Diamond: 30.9% (Mythos 5 specifically)
GDPval-AA Elo: 1932 (ranked #1)
Humanity's Last Exam: 53% (7+ point advantage)
Intelligence Index: 64.9 (roughly 5 points ahead of GPT-5.5)

Specialized Strengths

Software Engineering: Exceptional performance on complex coding tasks Knowledge Work: Superior performance on agentic, real-world knowledge tasks Scientific Research: Advanced capabilities in research and analysis Vision Tasks: Enhanced multimodal capabilities Long-Horizon Tasks: Performance improves with task length and complexity

Deployment Models

Claude Fable 5 (General Availability)

Same underlying model as Mythos 5 with added safeguards
Transparent fallback routing for risky queries
Immediate ecosystem integration
Subject to controversial policy changes

Claude Mythos 5 (Restricted Access)

Full capabilities without general availability safeguards
Limited access model for specialized use cases
Higher performance ceiling on certain benchmarks

Policy Changes

The introduction of Mythos-class models coincided with significant policy shifts:

data-retention-policy: 30-day mandatory retention for all Mythos-class traffic

Elimination of Zero Data Retention (ZDR) promise
Both first-party and third-party surfaces affected
Privacy protections including access logging and guaranteed deletion

silent-interventions: Invisible capability limitations for frontier AI development

Affects ~0.03% of traffic, concentrated in <0.1% of organizations
No user notification for effectiveness limitations
Implemented via prompt modification, steering vectors, or PEFT

Technical Architecture

Multi-Agent Orchestration

claude-managed-agents: Built-in delegation to smaller models Resource Optimization: Automatic selection of appropriate model sizes for subtasks Hierarchical Processing: Complex task decomposition and management

Safety Architecture

fallback-routing: Transparent routing to Opus 4.8 for certain risky queries Risk Assessment: Real-time evaluation of query safety implications Transparent Interventions: User notification for visible safety measures

Pricing and Access

API Pricing: $10/million input tokens, $50/million output tokens Cache Pricing: $12.50/million cache writes, $1/million cache reads Subscription Access: Initially included in Pro, Max, Team, and Enterprise plans Capacity Constraints: Temporary rollback to usage credits due to demand

Performance Characteristics

Resource Profile: "Slow, expensive, and capable" Token Usage: Routinely consumes 500K-1M tokens per session Session Duration: Multi-hour execution periods common Cost-Effectiveness: High per-token cost but potentially efficient per-outcome

Ecosystem Integration

Immediate deployment across major platforms:

cursor: CursorBench SOTA at 72.9%
devin: Integrated into Cloud Ultra, Desktop, and CLI
notion, Microsoft Foundry, GitHub Copilot
cline, Replit, Base44, magicpath, Arena, MCP Atlas

Industry Impact

Capability Scaling

Demonstrated viability of 2x parameter scaling
Established new performance ceilings across benchmarks
Validated objective-based workflow paradigms

Policy Precedents

First capability-based data retention requirements
Introduction of invisible safety interventions
Differentiated access models for same underlying technology

Competitive Response

Pressure on competitors to match capability levels
Industry debate over privacy and transparency policies
Questions about sustainable scaling trajectories

Future Implications

Mythos-class models represent a significant milestone in AI development:

Scaling Validation: Proof that larger models deliver meaningful capability improvements
Policy Evolution: New frameworks for balancing capability and safety
Workflow Transformation: Shift toward objective-based AI collaboration
Economic Models: High-capability, high-cost AI services